1. Field of the Invention
The invention relates to an evaluation method for determining the time stationarity of measured physiological, in particular cardiologic, signals, which can be implemented preferably on implantable cardiologic devices, such as defibrillators, heart pacemakers or comparable devices.
2. Background Art
For comprehensibility of the present invention, the background must be explained in detail. The most important method of obtaining information on the cardiovascular system resides in recording the ECG by electrodes that are attached to a patient""s body surface. Alternatively, a suitable measuring arrangement that includes an implant connected to an electrode may help record an intracardiac ECG or a signal that reflects the course of contraction in an entirely different way, such as intracardiac impedance. As a rule, the morphology of the signal course is evaluated for pathologic processes to be diagnosed or suitable therapeutic measures to be taken.
If any discrete parameters are extracted from the continuous ECG signal, such as the distance of the R wave of the heartbeat at an instant from the R wave of the preceding heartbeat (RR intervals), and if the sequence of values thus produced is outlined in dependence on the number of consecutive heartbeats measured, one will obtain a time series of this cardiac parameter.
It is assumed that in addition to accidental influences, the time series substantially reflects the global regulatory behavior of the cardiovascular system, i.e. the time series, in an abstract manner, gives information on internal transfer functions between components, on latencies of the information transfer between sensors and actuators, or on the working points of the internal transfer functions.
Attempts are made to characterize the status of the cardiovascular system by modeling from time series. This serves various purposes, such as risk stratification or prediction of potentially lethal cardiac events by a defibrillator, or gauging an adaptive frequency cardiac pacemaker. For correct modeling to be ensured, it is necessary to eliminate time series that are influenced by accidental events or during which a patient""s condition of physical or psychic stress changes during the period of observation. In the first case, a deterministic model produced from the measurement does not represent the factual condition of the cardiovascular system. In the second case, this condition is not defined at all, owing to a drift or a sudden rise of a parameter of the cardiovascular system. These considerations show that, with a view to obtaining a criterion of stationarity, a selection of certain segments of a time series is a fundamental requirement for modeling and thus for characterizing certain conditions.
So as to be employed in electronic implants, a test for stationarity is required to exhibit, as further decisive criteria, the ability of being automated and the greatest possible simplicity, since the computing capacity is restricted in such implantsxe2x80x94at least according to the current state of the art. This is fundamentally due to the fact that as compact as possible computing components must he used which can operate only at an energy supply of limited capacity. As will be explained below, the invention proceeds from these problems.
Regarding the problems posed by the conditions of stationarity upon measurement of any time series, standard tests are known, which are based on a complicated statistical evaluation of the time series and which, as such, do not appear suitable to implementation in implanted cardiologic devices of the current state of processor engineering. However, they will be explained in connection with the invention in order to illustrate the influence of disturbances of stationarity in a time series segment on the evaluation itself. Disturbances of stationarity of a segment can be divided into random and dynamic disturbances. Random disturbances comprise events produced in the heart that are not subject to regulation by the cardiovascular system, such as a temporary atrioventricular block or extrasystoles, i.e. contractions of the ventricle that are not due to sinus node excitation. Random disturbances result from the reception of stimuli from the surroundings, such as sudden noises, but also from inner processes, such as motivations, changes of attention or dreams, Dynamic disturbances result for instance from varying physical and mental stresses over the day that are due to a candidate""s activities and go along with varying metabolic demand.
Dynamic disturbances can largely be avoided in physiological experiments. Conventionally, this is achieved by the test conditions being correspondingly prepared, i.e. the candidate is examined in an environment of poor excitation and simultaneously invited to show a passive behavior. However, the examination as such causes multiple psychic processes which can be mastered only by massive drugging. This will interfere with the examination of the regulatory behavior of the cardiovascular system that is affected by the drugging.
Therefore, strictly speaking, preparing test conditions is not possible, and a stationary segment cannot be defined xe2x80x9ca priorixe2x80x9d, which is all the more true for an implant of autonomic operation. In clinical tests, stationary measuring intervals are mostly determined xe2x80x9cby inspectionxe2x80x9d and xe2x80x9ca posteriorixe2x80x9d, i.e., for further evaluation, the experimenter selects, from a given time series, a segment as being stationary during which, in his individual view, the signal seems to have a more uniform course than in the remaining segments. The disadvantages of this way of proceeding reside in the subjectivity of this evaluation method, which is primarily based on the lack of quantitative criteria. Moreover, a range may be defined as stationary that is in fact not stationary, which will render any subsequent evaluations faulty. Finally, any subjective definition of measuring intervals that are assumed to be stationary will make various patients comparable only to a limited degree. Also, by nature, subjective evaluation methods of this type cannot be automated.
For identification of the stationary conditions that are necessary for modeling and thus for general diagnostics, and for elimination of nonstationary measuring segments, a method is required that enables the stationarity to be determined xe2x80x9ca posteriorixe2x80x9d and that simultaneously obeys to the following criteria:
The method must be objective, it must make available a criterion for the fundamental existence of a stationary condition, and it must be quantifiable, comparable and capable of being automated.
In addition, the method is desired not to be based on pure empirism, but to produce a reference to physiological modifications that accompany nonstationary cardiovascular behavior, for the specificity of the test to be augmented.
The simplest type of a statistic test for stationarity is based on the comparison of empiric distributions in two successive segments. The statistic moments of the distributions are determined and compared by means of so-called xe2x80x9ctwo sample testsxe2x80x9d. If a significant difference results between the moments of the two distributions, the assumption of stationarity is rejected. This may be a very simple method, but it interprets the cardiovascular system as a purely stochastic system, i.e. as a system having an unlimited number of internal degrees of freedom. Moreover, varying conditions of the cardiovascular system are represented by identical distributions.
Assuming that, apart from external and internal random disturbances, the cardiovascular system obeys to deterministic conditions of development, i.e that the subsequent heartbeat is precisely determined by all the preceding heartbeats, the nonlinear dynamics provide various stationarity tests. Since, however, they are based on the so-called correlation integral, they are very complicated, requiring a well-founded knowledge on a number of unknown parameters and the existence of comparatively long stationary intervals. Moreover, the assumption of a strictly deterministic system as a basis of regulation of the cardiovascular system is definitely as unrealistic as the total randomness thereof. Since the great number of internal degrees of freedom is presumably not entirely coupled, internal system noise is to be expected even when all stochastic disturbances are entirely eliminated.
It is more realistic to assume that correlations play a major role in the cardiovascular system, which are however superposed by internal noise even in the stationary condition. For empirical purposes it is not necessary to specify these components in detail; it is sufficient to find a method for a stationarity test that will take both factors into account. A combination of stochastic behavior with deterministic correlations is grasped by the concept of the fractional Brownian motion (fBm) described in the following.
If the course of a cardiac time series is interpreted as the motion oriented in time of an individual particle that can walk to various places in the direction of the y axis, you will obtain a random walk or a one-dimensional Brownian motion. This motion is equal to the ordinary stochastic Brownian motion of for instance a particle, if the respective step of motion takes place totally independently from all the preceding steps. But if the respective step depends partially on the motion of prior steps, this is called a fractional Brownian motion. In this case, two fundamentally different cases occur. The current step of motion may take place in the direction of the single or of several prior steps or it may be opposite to them. The stronger the one or the other probability is distinguished, the more this will characterize the course of the time series in comparison to the Brownian motion, in which the probability for both directions is identical. The type of this correlation is measured for instance by the Hurst scaling exponent H, which may range between 0.0 and 1.0. A high value corresponds to a very smooth course of the time series, the probability being great that the current step is oriented in the direction of the preceding step. With a low H value, the time series appears xe2x80x9croughenedxe2x80x9d. This is still going to be explained on the basis of a corresponding illustration of simulated time series in the exemplary embodiment (cf. FIG. 1).
In the present context, a verification of measuring-value time series for stationarity proceeds from the assumption that two segments of a time series represent identical conditions of the system when the Hurst scaling exponent thereof has the same value. In this case, the system is assumed to be stationary during both segments. A threshold must be given, below which a difference can be assumed as not yet significant. This threshold is determined empirically or from simulations. Attention must be paid to the fact that the realization of a fractional Brownian motion includes a stochastic component in spite of the correlations and that two time segments can never be distinguished accurately and at a hundred percent sensitivity.
The difficulties, resulting therefrom, in distinguishing various segments are going to be explained in detail in the following description of the exemplary embodiment, taken in conjunction with FIG. 2.
The Hurst scaling exponent may be fundamentally suitable for the evaluation of correlations and thus for a test for stationarity of time series, however the determination of this exponent by known standard methods is complicated, which means that implementation on an implanted cardiologic device cannot be put into practice. Moreover, accurate quantification of the Hurst scaling exponent by standard methods is difficult in particular for shorter time series as they are relevant predominantly in connection with cardiologic measuring values in the case of application in implantable devices.
Proceeding from the prior art problems, it is an object of the invention to specify an evaluation method that can be implemented on implantable cardiologic devices and which offers a simple way of determining, by restricted computing capacity, the time stationarity of measured cardiologic signals.
This object is attained by an evaluation method comprising the following steps:
recording the values of a cardiologic signal during a certain measuring period;
dividing the obtained time series of signal values into individual segments;
mapping successive subsets of the individual signal values in a segment on associated symbol series according to the criterion of whether a respective signal value increases or decreases as compared to the preceding signal value;
determining the frequency of varying symbol series;
determining a testing parameter which reflects the frequency ratio within a segment of correlated symbol series to anti-correlated symbol series;
determining the relative difference of the testing parameters of two adjacent segments; and
comparing the relative difference of two testing parameters with a given threshold as a criterion for the stationarity of the time series of the recorded physiological signal.
The above method can be subsumed under the term xe2x80x9csymbolic dynamicsxe2x80x9d as a keyword. A time series is mapped on a sequence of discrete symbols, with the information included in the time series being compacted. Since the information, under regard, of cardiac time series comprises correlations and anti-correlations, the symbols used here only illustrate the respective directions of the xe2x80x9cmotionxe2x80x9d B(H) that represents for instance the respective RR interval. If B(H)xe2x80x94i.e. the signal valuexe2x80x94increases, then the symbol xe2x80x9c1xe2x80x9d is allotted to the step; if B(H) decreases, the symbol xe2x80x9c0xe2x80x9d corresponds to this.
Mapping the individual signal values B(H) of a time series on bit words of a defined bit number comprising the elements 0 and 1 preferably corresponds to a function of representation
Bn(H)=1 for Bn+1(H)xe2x88x92Bn(H)xe2x89xa70
and
Bn(H)=0 for Bn+1(H)xe2x88x92Bn(H) less than 0.
In order to examine whether the direction is changed more frequently or whether it is maintained, bit words are formed, having preferably five symbols, such as xe2x80x9c01010xe2x80x9d, which corresponds to a distinct anti-correlation. The frequency of words that correspond to a predominant anti-correlation are related to the frequency of words that represent a correlation. This relation determines a testing parameter, the value of which is compared for two intervals, furnishing a quantitative reference scale.
Realization by the aid of another method that measures stochastic correlations takes place analogously, the difference residing in that the testing parameter has to be suited to the respective method.
In conclusion, the difference between symbolic dynamics and direct computation of H resides in the missing consideration of long range correlations, which are however less relevant given the short segments of the physiologic stationary time series that are to be examined. The property of self-similarity that defines a fractional Brownian motion is not strictly examined either, which is of minor interest for the practical purpose here envisaged. An advantage of symbolic dynamics resides in that extrasystoles are not disproportionately weighted, owing to their height. Therefore there is no need of filtering. Due to the short-long sequence in extrasystoles, a great number of extrasystoles may however lead to the correlation being shifted in the direction of reverse relations between the individual steps of the motion. Further adaptation of the word length on an empiric basis seems conceivable.
Finally, the invention relates to an implantable cardiologic device, in particular a defibrillator or cardiac pacemaker, having an operation control program with the described evaluation method according to the invention implemented.